We think it's bad, but do we know what we're looking for? Moving toward a measure of early specialization in sport
Bibliographic record
Abstract
Early specialization in sport has been linked to negative consequences, such as injury, burnout, and attrition. However, research has been hampered by the lack of a standardized definition and reliable measurement of sport specialization (Jayanthi et al., 2015). Recently, the American Orthopaedic Society for Sports Medicine reached consensus on a definition of early sport specialization comprising three criteria: 1) participation in intensive training and/or competition in organized sports for more than 8 months per year, 2) participation in one sport to the exclusion of participation in other sports, and 3) involving prepubertal children (around age 12 years) children (LaPrade et al., 2016). This study aimed to lay the groundwork for the development and validation of a measure of early specialization in sport, based on this new definition and other previously used measures. Secondary data analysis was conducted on retrospective-longitudinal survey data collected from 255 swimmers (Mage = 13.8 years; range = 12-17). Data included detailed descriptions of sport backgrounds, including season durations, frequency of weekly practices, and total weekly hours in each sport from age 6. Exploratory factor analyses determined how eight particular survey items represented data for swimming specialization, their communalities and loadings on a single factor, and measures of internal consistency reliability. We employed this single factor score to plot the developmental profile of swimmers from 6-17 years of age. We discuss the utility of such a measure, how it could be used in path analyses, and whether its operationalization is suitable based on evolving definitions of specialization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".